Artificial intelligence is rapidly becoming a powerful ally in the neurology clinic, capable of interpreting the complex electrical signals of electroencephalograms — or EEGs — and giving doctors a faster, sharper tool to diagnose neurological disorders and detect early warning signs of diseases that have not yet surfaced.
The LVIS Corp., headquartered in Palo Alto, California, recently announced advancements to its NeuroMatch platform, a software system that can process large, complex data sets to help neurologists make more accurate diagnoses.
The manual review process is traditionally time-consuming. A single ambulatory EEG study can generate tens of thousands of segments that must be reviewed by specialists. LVIS says that through NeuroMatch’s new ambulatory EEG functionality — embedded with AI — the company is removing many of the inefficiencies that have long slowed the review process. The browser-based platform automatically identifies and flags clinically relevant neurological events while filtering out irrelevant things such as muscle and eye movements.
“Ambulatory EEG is essential, but it’s been incredibly resource-intensive for both physicians and technicians,” said Jin Hyung Lee, founder of LVIS Corp. “With this NeuroMatch update, we’ve built an intelligent, streamlined experience that lets clinicians focus only on what matters most—making faster, more confident diagnoses.”
According to LVIS, healthcare providers will be able to “launch, monitor, and interpret ambulatory EEG studies from any internet-connected device, at any time.”
“Physicians can start a review in the hospital, continue from home, and finalize reports from their office — all without interrupting workflow or toggling between systems. Reports can be signed, summarized, and delivered within the same platform. Cloud-based infrastructure allows teams to collaborate across facilities without compromising quality or data consistency.”
Early detection is key with conditions such as dementia, which, according to the World Health Organization, affects more than 57 million people worldwide and is projected to increase to 139 million by 2050.
“Neuroscience is moving beyond general symptom observation. New approaches focus on directly measuring brain function itself — especially through the analysis of brainwave patterns. With specialized EEG-based technologies, it becomes possible to identify small, early disruptions in brain activity. These subtle shifts often occur long before a patient notices memory loss, confusion, or seizures. This evolution could redefine how we treat conditions like epilepsy, dementia, and depression — moving from managing damage to actively protecting and preserving brain health. Instead of waiting for cognitive health decline, we can intervene earlier, customize care, and potentially slow or even prevent the most devastating effects of brain disease.
“Artificial intelligence isn’t just a buzzword at LVIS, but a powerful engine driving a new standard of care. Our technologies can process massive amounts of brainwave data rapidly and accurately, providing insights that would be impossible to extract manually. This supports faster diagnosis, better disease tracking over time, and the ability to adjust treatments dynamically based on near real-time brain health measurements — something traditional methods could never accomplish.”
AI is being applied to virtually every area of medicine, from identifying new treatments and developing new drugs to helping physicians write notes during physical examinations.
Microsoft, for example, has developed the Microsoft AI Diagnostic Orchestrator, or MAI-DxO, which turns any language model into a panel of physicians with diverse diagnostic approaches. The tool can ask follow-up questions, order tests or deliver a diagnosis, as well as run cost checks and verify its own reasoning.
“We believe that orchestrating multiple language models will be critical to managing complex clinical workflows. Orchestrator can integrate diverse data sources more effectively than individual models, while also enhancing safety, transparency, and adaptability in response to evolving medical needs. This model-agnostic approach promotes auditability and resilience, key attributes in high-stakes, fast-evolving clinical environments. MAI-DxO boosted the diagnostic performance of every model we tested. The best performing setup was MAI-DxO paired with OpenAI’s o3, which correctly solved 85.5% of the NEJM (New England Journal of Medicine) benchmark cases. For comparison, we also evaluated 21 practicing physicians from the U.S. and U.K., each with 5–20 years of clinical experience. On the same tasks, these experts achieved a mean accuracy of 20% across completed cases. MAI-DxO is configurable, enabling it to operate within defined cost constraints. This allows for explicit exploration of the cost-value trade-offs inherent in diagnostic decision making. Without such constraints, an AI system might otherwise default to ordering every possible test — regardless of cost, patient discomfort or delays in care. Importantly, we found that MAI-DxO delivered both higher diagnostic accuracy and lower overall testing costs than physicians or any individual foundation model tested.”
